Highway system involves itself in national economy and is an emerging industry in China. By 2010, China's total highway mileage will exceed 80,000 km. Jiangsu Province is China's economically developed region and is currently a leader of the country's Intelligent Transportation System (ITS). Jiangsu has 3,558 km of existing expressways with a highway density of 3.46 km/100 square km. By the end of 2010, its total highway mileage is projected to reach 5,000 kilometers.
China's landscape extends from plains, rivers, and lakes to other land systems. In the middle and western regions of China, the topography of hills and terrains is complex and disastrous weather conditions such as fog, haze are frequent occurrences. These hazardous weather conditions occurring at uncertain times and places create great risks to highway transportation, especially to vehicle safety concerns. In 1975, highways from California to New York in the United States resulted in more than 300 vehicles collisions and killing more than 1,000 people because of heavy fogs and these are considered to be the world's most serious traffic accidents. 1986 in France alone, there were 1,200 accidents (excluding urban areas) attributed to fogs, causing 182 dead and 175 injuries, with 1,352 people slightly injured. Although accident rate due to fog on highways is 4% annually, the mortality rate is as high as 7% to 8%. In terms of measurement and management, the Shanghai-Nanjing Expressway has about 10 meteorological observation stations for a cost of nearly ten million. It is still difficult to accurately detect the occurrence of fog in certain areas.
To deal with the low visibility problem, China's highway management departments initiate road closures to reduce traffic accidents. Because of the subjectivity and lack of quantitative indicators, the implementation of traffic control and management procedures are not scientific and not sufficiently standardized to be efficient, sometimes this procedure of road closures is even counterproductive. To this end, real-time meteorological road condition monitoring, especially timely detection and reporting of low visibility condition is the key to enhance our ability to respond to disastrous weather conditions, to reduce losses and to improve highway management efficiency.
Currently installed highway meteorological visibility monitoring and testing equipment is mainly conventional laser visibility meter-based, generally available atmospheric transmission analyzer and scattering instrument. These two types of equipment can cause large number of errors in the condition of heavy rain, fog and other low visibility weather due to moisture absorption and difficulty of normal observation. It is difficult to accurately detect the occurrence of fog in certain areas. Additionally, high manufacturing cost and maintenance cost of these meteorological monitoring stations make them hard to be popularized and to be implemented in a wide area. For instance, the 10 or so meteorological monitoring and detection stations along Shanghai-Nanjing Expressway cost nearly ten million.
Video visibility detection technology utilizes video image analysis and artificial intelligence, combined with traditional atmospheric optical analysis, to analyze and process video images to establish the relationship between images and the real-time weather condition. Meteorological visibility values are calculated by measuring the changes of image characteristics. Compared with traditional methods, this detection method basically resembles human eyes in viewing objects. This technology possesses the characteristics of low-cost, easy of operation, and compatibility with the cameras already operating along the roadsides, which has the advantages of existing wide area coverage. However, this is a new technology and needs improvement.
At present, there are very few studies being conducted outside of China. Most of them are still in the theoretical development and experimental stage. University of Minnesota in the United States proposed a video visibility detection method using fixed distance from the target object[1]. The need of artificially preset multiple video detection targets, high cost, complicated operation, vulnerability to the effects from terrain and other environmental factors are all limitations of this method. A team from MIT put forward a method to calculate relative visibility based on logo images[2]. This method obtains relative visibility by comparing the detected scene images to pre-stored images of known meteorological visibility. This method does not need auxiliary facilities and is easy to use. But it is difficult to use this method with PTZ cameras and it is susceptible to interferences from moving objects. Swedish National Road Administration Center had proposed a visibility detection method based on neural network and infrared video imaging[3]. This method extracts visibility reading from different edges of the images, classifies them using neural network algorithm, and converts the results to corresponding visibility levels. These infrared cameras have relatively low operating noise but they are expensive and complex to maintain, therefore it is difficult to install these infrared cameras in a reasonable density along the road.
Reference [4] proposed a visibility detection method based on the visibility of road markers. This method uses a detecting and matching algorithm with image segments from a preset target to obtain its characteristics and arrives at corresponding visibility values. This method requires the installation of additional markers and resulting in higher cost. In addition, the detection range and accuracy of this method is limited by the field of view and the distance and number of the targets. It is also difficult to retrofit existing PTZ cameras to be compatible with this method. Reference [5] talked about a detection method based on video image contrast analysis. By analyzing and contrasting each pixel and its neighboring pixels, a condition of the selected maximum value larger than a given threshold value indicates a human eye distinguishable image. Combined with the camera calibration, a visibility value is calculated. Because of the threshold value division, this method is susceptible to noise, including the lane division line area noise and CCD imaging current noise. In particular, quantification error and noise of the procedure can lead to hopping results and the algorithm is not stable enough.